Abstract
K-means (or called hard c-means, HCM) and fuzzy c-means (FCM) are the most known clustering algorithms. However, the HCM and FCM algorithms work worse for the data set with different shape clusters in noisy environments. For solving these drawbacks in HCM and FCM, Wu and Yang (2002) proposed alternative c-means clustering that extends HCM and FCM into alternative HCM (AHCM) and alternative FCM (AFCM). In this paper, we further extend AHCM and AFCM as Gaussian-kernel c-means clustering, called GK-HCM and GK-FCM. Some numerical and real data sets are used to compare the proposed GK-HCM and GK-FCM with AHCM and AFCM methods. Experimental results and comparisons actually demonstrate these good aspects of the proposed GK-HCM and GK-FCM algorithms with its effectiveness and usefulness in practice.
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Yang, MS., Chang-Chien, SJ., Nataliani, Y. (2018). Gaussian-kernel c-means Clustering Algorithms. In: Fagan, D., MartÃn-Vide, C., O'Neill, M., Vega-RodrÃguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2018. Lecture Notes in Computer Science(), vol 11324. Springer, Cham. https://doi.org/10.1007/978-3-030-04070-3_10
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DOI: https://doi.org/10.1007/978-3-030-04070-3_10
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